| Literature DB >> 25018671 |
Anupama Kuruvilla1, Jian Li2, Pablo Hennings Yeomans1, Pedro Quelhas3, Nader Shaikh4, Alejandro Hoberman4, Jelena Kovačević5.
Abstract
We propose an automated algorithm for classifying diagnostic categories of otitis media (middle ear inflammation); acute otitis media, otitis media with effusion and no effusion. Acute otitis media represents a bacterial superinfection of the middle ear fluid and otitis media with effusion a sterile effusion that tends to subside spontaneously. Diagnosing children with acute otitis media is hard, leading to overprescription of antibiotics that are beneficial only for children with acute otitis media, prompting a need for an accurate and automated algorithm. To that end, we design a feature set understood by both otoscopists and engineers based on the actual visual cues used by otoscopists; we term this otitis media vocabulary. We also design a process to combine the vocabulary terms based on the decision process used by otoscopists; we term this otitis media grammar. The algorithm achieves 84% classification accuracy, in the range or outperforming clinicians who did not receive special training, as well as state-of-the-art classifiers.Entities:
Keywords: classification; grammar; otitis media; vocabulary
Year: 2012 PMID: 25018671 PMCID: PMC4094109 DOI: 10.1109/ICIP.2012.6467492
Source DB: PubMed Journal: Proc Int Conf Image Proc ISSN: 1522-4880